Math Content Developer $100,000 USD/year Pay is set based on global value, not the local market.
Most roles = hourly rate x 40 hrs x 50 weeks
Description
Fast Math is not designed to increase worksheet volume in the hope that repetition alone will accelerate students. Your responsibility is to pinpoint the precise barriers preventing students from achieving math fluency, then leverage AI to reconstruct the learning pathway until accurate computation becomes second nature.
2 Hour Learning enables students to master core academics in significantly less time than traditional school schedules. Your focus will be the Fast Math program, where you will analyze student performance metrics, platform analytics, assessment outcomes, and student work samples to uncover obstacles to fluency and fact mastery. These obstacles may include inadequate sequencing, inappropriate practice structures, absent scaffolding, insufficient feedback, or interventions that do not address fundamental misconceptions.
This role is intensely practical. You will deploy LLMs, AI-driven content creation, no-code workflows, and agentic AI systems to refine practice progressions, instructional explanations, assessments, scaffolding, and corrective interventions. Success is measured by students transitioning from labored calculation to rapid, precise, automatic execution. If you are prepared to apply AI daily to achieve quantifiable improvements in mathematical fluency, submit your application now.
What you will be doing
- Analyze persistent deficiencies in math fluency, fact retention, speed, and precision through performance metrics, assessments, platform analytics, and student work samples.
- Pinpoint barriers to automaticity, including inadequate sequencing, missing scaffolding, suboptimal practice patterns, ineffective feedback, flawed assessments, or unsuccessful interventions.
- Deploy LLMs, no-code workflows, and agentic AI systems to redesign practice progressions, instructional explanations, scaffolding, and corrective interventions for Fast Math.
- Refine questions, answer explanations, distractors, rubrics, and assessments to reveal misconceptions and provide accurate mastery measurement.
- Verify AI-generated or AI-modified content for mathematical precision, grade-level appropriateness, clarity, rigor, and consistency with Alpha's learning science framework.
What you will NOT be doing
- Creating repetitive drill activities without documented evidence of improved fluency or automaticity.
- Developing isolated lesson plans that appear refined but fail to influence mastery metrics.
- Limiting yourself to analysis. You will identify the learning obstacle, reconstruct the instructional material, and verify the impact of your modifications.
- Overseeing a conventional curriculum initiative, performing administrative duties, or navigating prolonged approval workflows.
- Developing educational software applications. Your concentration is the mathematics learning experience, not software engineering.
Key responsibilities
Drive K-12 math fluency forward by systematically enhancing Fast Math curriculum, practice activities, assessments, scaffolding, and interventions until students perform calculations with accuracy and automaticity.
Candidate requirements
- Bachelor's degree or higher in Mathematics, Statistics, Applied Math, Engineering, Physics, or another quantitative field.
- 3+ years of experience in math instruction, math curriculum development, assessment development, educational content creation, or learning design, especially focused on building math fluency and fact mastery and building students toward automaticity in four-function math.
- Strong K-12 math subject expertise, including accuracy, rigor, sequencing, misconceptions, fluency development, and grade-level expectations.
- Practical knowledge of learning science or instructional design, such as mastery learning, direct instruction, scaffolding, cognitive load, retrieval practice, or deliberate practice.
- Experience using AI tools to improve educational work, such as LLMs, AI-assisted content generation, no-code workflows, coding/API-based tools, or agentic AI tools.
- Ability to use student learning data to identify barriers to fluency and improve curriculum, assessments, practice, scaffolding, or interventions.
- Excellent written communication skills, especially explaining mathematical concepts clearly and precisely.
Nice to have
- Direct experience designing programs or interventions focused on arithmetic fluency, fact mastery, or automaticity.
- Experience with LLM APIs for content generation, data analysis, or adaptive learning work.
- Experience building or improving math assessments that surface misconceptions, not just procedural accuracy.
- Experience sharing education, math, or AI insights in an online community, such as Twitter/X, LinkedIn, or a public knowledge base.
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